LayoutDM：可控布局生成的离散扩散模型

可控布局生成旨在合成具有可选约束条件（如特定元素的类型或位置）的元素边界框的合理排列。在本研究中，我们试图在一个基于离散状态空间扩散模型的单一模型中解决广泛的布局生成任务。我们的模型名为LayoutDM，自然地处理离散表示中的结构化布局数据，并学习逐步从初始输入中推断出无噪声的布局。我们通过模态方式的离散扩散建模布局损坏过程。对于条件生成，我们建议在推断过程中以掩码或逻辑调整的形式注入布局约束。实验结果表明，我们的LayoutDM成功生成高质量的布局，并在几种布局任务上优于特定任务和任务不可知的基线。
Controllable layout generation aims at synthesizing plausible arrangement of
element bounding boxes with optional constraints, such as type or position of a
specific element. In this work, we try to solve a broad range of layout
generation tasks in a single model that is based on discrete state-space
diffusion models. Our model, named LayoutDM, naturally handles the structured
layout data in the discrete representation and learns to progressively infer a
noiseless layout from the initial input, where we model the layout corruption
process by modality-wise discrete diffusion. For conditional generation, we
propose to inject layout constraints in the form of masking or logit adjustment
during inference. We show in the experiments that our LayoutDM successfully
generates high-quality layouts and outperforms both task-specific and
task-agnostic baselines on several layout tasks.
论文链接：http://arxiv.org/pdf/2303.08137v1